# Anatomically-Informed Multiple Linear Assignment Problems for White Matter Bundle Segmentation

## TL;DR

This paper introduces an advanced bundle segmentation method that combines anatomical and geometric information, significantly improving accuracy especially for small white matter bundles.

## Contribution

It extends a linear assignment problem approach by integrating anatomical priors, enhancing segmentation performance over existing methods.

## Key findings

- Significant improvement in segmentation accuracy, especially for small bundles.
- Effective integration of anatomical and geometric information.
- Outperforms previous state-of-the-art methods.

## Abstract

Segmenting white matter bundles from human tractograms is a task of interest for several applications. Current methods for bundle segmentation consider either only prior knowledge about the relative anatomical position of a bundle, or only its geometrical properties. Our aim is to improve the results of segmentation by proposing a method that takes into account information about both the underlying anatomy and the geometry of bundles at the same time. To achieve this goal, we extend a state-of-the-art example-based method based on the Linear Assignment Problem (LAP) by including prior anatomical information within the optimization process. The proposed method shows a significant improvement with respect to the original method, in particular on small bundles.